Search

Procrastination is often characterized as minimal progress initially, with a significant increase in progress shortly before the deadlines. Yet, the cognitive mechanisms underlying this intriguing dynamic feature of procrastination—the time course of progress—remain poorly understood. We investigated this through an experiment where participants worked on a self-paced, week-long online reading task consisting of numerous work units (N = 611). We proposed two models that fit each individual's time course of progress. Both models consider the time course of progress as the output of sequential decision-making: whether to work now (and, if so, how much) or later. The first, a normative model, calculates the value of making progress using the Bellman equation; the second, a roll-out model, estimates this value by simulating future work progress. We found that the rollout model fit the data much better, suggesting some evidence against people behaving rationally and some evidence for people simulating future work progress.

Procrastination is universal but not monolithic; a second dimension, the Intention–Behavior Gap (IBG), has been suggested to be relevant. The IBG is the difference between one’s intentions and one’s behavior. So far, the IBG has not been directly measured within a behavioral experiment. We assigned subjects a week to work on a lengthy online reading task. Before the task started, subjects reported their plan by the number of reading units for each day. We found great variability in their plans, actual time course and the difference between the two (IBG). The time course of work ranged from completing earlier than planned to not finishing. We calculated correlations between the mean unit completion day, the IBG, the Causal Dimension Scale, indices of regret and satisfaction, and scales for procrastination (General, Active and Irrational). Besides the correlations expected based on definitions, we did not find any significant correlations.

Procrastination is ubiquitous, but how to reduce procrastination is rarely examined in a behavioral experiment. We hypothesized that procrastination is reduced when the reward is immediate versus delayed, and that the size of reduction could vary across different levels of pressure on task completion. To test our hypothesis, Subjects were assigned a week to work online on a lengthy reading task. We manipulated the reward timing (delayed versus immediate) and levels of task completion pressure. We quantified the level of procrastination with mean completion day of a paragraph, with a higher value indicating a higher level of procrastination. We found that among subjects who completed the task, there was no main effect of reward timing on procrastination level, whereas subjects who left the task unfinished procrastinated less when reward was immediate. There was no main effect of task completion pressure, and no interaction between reward timing and task completion pressure.

As an important force to guide future economic and social development, strategic emerging industries are the core of China's modern industrial system that helps in achieving high-quality economic development. The study sample includes 1,109 most representative listed enterprises in strategic emerging industries and their associated 19,540 enterprises. This study has depicted the characteristics of the spatial distribution of China's strategic emerging industries from the perspectives of industrial clustering and industrial networks and provides an in-depth analysis of cluster structure, network centrality, network heterogeneity, and their spatial differentiation. This study has four main findings. First, in the eastern region, the core and supporting enterprises of strategic emerging industries are relatively balanced and the industry clusters are more complete, while in the central and near-western regions, there are relatively few core enterprises and they are mainly concentrated in a few provincial capitals. In the northwest and northeast regions, there are not enough core and supporting enterprises to support the development of strategic emerging industry clusters. Second, China's strategic emerging industry network has a diamond-shaped network pattern with the Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta, and Chengdu-Chongqing regions at the apex, and several vertically linked industry networks that have been formed by core cities. Third, the core companies' key investments in China's strategic emerging industries are concentrated in three key segments of the industry chain: manufacturing, R&D, and information (20.42%, 19.50%, and 17.56%, respectively). Based on this, two major industrial networks-the R&D and information services network and the manufacturing network and three national hubs-Beijing, Shenzhen, and Shanghai, were formed. Beijing is the core node of the R&D and information services network, whereas Shenzhen and Shanghai are the core nodes of the production and manufacturing networks. In addition, Hangzhou is gradually becoming an information service center city with national influence, while Guangzhou, Chengdu, Wuhan, and Xi'an remain regional information service centers and manufacturing leading centers. Finally, there is a significant positive correlation between the size of industry agglomerations and the centrality of the industry network. When the cluster size is small, cluster size expansion primarily supports the expansion of the breadth of industrial network linkages, whereas when the cluster size is large, it primarily supports the increase in linkage intensity. More importantly, when industry agglomerations reach a certain size, the rapid increase in the strength of industrial network linkages is more often due to small and medium-sized supporting enterprises than due to large core enterprises. This study provides a comprehensive understanding of the characteristics and spatial patterns of China's strategic emerging industries as well as the problems faced by different regions. Moreover, an analysis of the heterogeneity of industrial networks, especially the differences between the core node cities of different networks, also demonstrates the variability of the impact of different types of industrial networks on different cities and regions.

Procrastination is ubiquitous, but its underlying processes are poorly understood. Studies of procrastination have used self-report questionnaires and therefore have limited use as the basis for process models. As typical procrastination behavior is characterized by a delay in starting the work and rushing in the end, we argue that studies should emphasize the time course of progress. We designed a reading task to quantify the time course of procrastination. Subjects were given seven days to work online on a boring and lengthy reading task. We tested whether reward delay is a predictor of procrastination. We introduce a metric for quantifying the degree of procrastination from the time course of progress. The degree of procrastination tends to be higher in the delayed reward condition than that in the instantaneous condition. We also observed great individual variation in the progress course. Further work needs to investigate what factors contribute to this variability.

Procrastination is prevalent. Empirical studies of procrastination have identified various contributing factors underlyingprocrastination. Models of procrastination, however, have only considered temporal discounting and have ignored otherfactors. Moreover, existing models of procrastination are mostly conceptual, and there is a lack of process models toexplain why people procrastinate. Here, we use reinforcement learning theory to build a process model of procrastination.The model assumes that people maximize expected utility while minimizing the total cost of the effort. Our model makesseveral predictions: 1. Strong temporal discounters will delay working early and rush to work near the deadline; 2. If atlow effort cost, cost is sensitive to increases in effort, people will delay working until the last minute; 3. If time pressure oreffort cost is high, perfectionists will not work at all. We designed a behavioral experiment to study the factors underlyingprocrastination and to test our model predictions.

Hospice is touted as an exemplary model for end-of-life care. However, there is little generalizable evidence estimating benefits of hospice at the national level. Using a national population-based probability sample of U.S. adults with weights applied (data collected Spring/Summer 2015), we conducted six logistic regression models to evaluate linkages between hospice involvement and end-of-life outcomes (pain, home death, presence of family, access to life-prolonging care, respect for spiritual/religious beliefs, financial burden) from N = 235 informal caregivers of decedents prior to death. Respondents were M = 55 years old (SD = 17), 55% female, 77% White, 91% non-Hispanic, and 71% had cared for a hospice recipient. In fully adjusted analyses, hospice users had better reported outcomes observed in two models: (a.) presence of family/friends at death (OR = 2.82, SE =.48, P <.05), and (b.) respect for spiritual/religious beliefs (OR = 9.52, SE =.73, P <.01). Hospice utilization was not statistically significant (P >.05) in all other adjusted models. Although more research is needed, findings support claims that hospice remains a high quality option for end-of-life care in the U.S. [ABSTRACT FROM AUTHOR]

Advanced care planning (ACP) utilization remains very limited in rural communities compared to urban areas. ACP earlier in the disease trajectory is particularly important for people with dementia (PWD) due to its progressive nature affecting their decision-making ability. Considering the well-documented benefits of ACP in improving the quality of end-of-life (EOL) care, the rural vs. urban disparity may indicate poorer EOL quality for rural PWD. This study aimed to explore barriers and current resources for ACP of PWD from the perspectives of health or social service providers serving rural communities. Using a qualitative approach, semi-structured face-to-face interviews were conducted with 11 health or social service professionals serving older adults and their caregivers in rural Alabama. Thematic analysis revealed three major barriers: (1) lack of knowledge, (2) psychosocial barriers, and (3) limited access to healthcare. Participants also showed misconception that a lawyer or a notary is required for ACP. Two themes arose in the participants' recommendations to address the barriers: (1) providing ACP-relevant information and (2) addressing psychosocial stressors about ACP. This study highlighted an urgent need for social policy in ACP education for caregivers and service providers in rural settings. [ABSTRACT FROM AUTHOR]

Over the years, much research has focused on industrial relocations between countries resulting from globalisation and industrial modernisation. However, the relocation decisions of individual firms within metropolitan areas remain an insufficiently studied aspect of the relocation literature. This study aims to fill this gap by examining the relocation intentions and locations of high‐tech manufacturing firms at various stages of development in Beijing from the firm life cycle perspective. The results show that larger firms with more employees and higher property income are more willing to relocate. Interestingly, smaller firms do not follow the incubator theory, instead relocating to non‐agglomerated areas. As firms grow, however, they tend to move to areas with higher firm concentration. This indicates that not all industrial clusters in cities function as business incubators. Furthermore, it also confirms the influence of rootedness, regional accessibility and regional policies on the relocation of firms. [ABSTRACT FROM AUTHOR]

Background: Despite documented benefits of Advance Care Planning (ACP), it is still under-utilized in the U.S. Our study aimed to examine whether experiencing a loved one's death is associated with one's own ACP behavior among adults in the U.S. and the potential moderating effect of age. Method: Using a nationwide cross-sectional survey design with probability sampling weights, our study included 1006 adults in the U.S. who participated in and completed the Survey on Aging and End-of-Life Medical Care. Three binary logistic regression models were established to investigate the relationship between death exposure and different aspects of ACP (i.e., informal conversations with family members and doctors and formal advance directives completion). The moderation analysis was subsequently conducted to examine moderating effects of age. Results: The exposure to a loved one's death was significantly associated with higher odds of having conversations with family about end-of-life medical care preferences among the 3 indicators of ACP (OR = 2.03, P <.001). Age significantly moderated the association between death exposure and ACP conversations with doctors (OR =.98, P =.017). The facilitation effect of death exposure on informal ACP engagement in discussing end-of-life medical wishes with doctors is stronger among younger adults than older adults. Conclusions: Exploring an individual's previous experience with a loved one's death might be an effective way to broach the concept of ACP among adults of all ages. This strategy may be particularly useful in facilitating discussions of end-of-life medical wishes with doctors among younger adults than older adults. [ABSTRACT FROM AUTHOR]

The classification of surrounding rock is crucial for formulating safe tunnel construction plans and support measures. However, the complex geological environment of tunnels presents a challenge in obtaining accurate drilling parameters for rock mass classification. This paper presents the development of a rock drilling testing system, which includes a propulsion speed acquisition system, oil pressure acquisition system, air pressure acquisition system, and an automatic data acquisition system. This system enables real-time, high-precision automatic collection and storage of parameters such as propulsion speed, with data collected twice per second for each parameter. Leveraging the Qingdao Metro Line 6 as a case study, we conducted rock mass drilling and constructed a rock mass classification database. By employing kernel density estimation and Pearson correlation analysis, we quantified the correlation between rock mass classification and the drilling parameters. The results indicated that relying on a single drilling parameter is insufficient for accurately determining rock mass classification. Both impact pressure and rotational pressure showed the strongest correlation with rock mass classification, each with a correlation coefficient below −0.8 (indicating a strong negative correlation). Outlier values of drilling parameters were excluded using the interval method. Based on the remaining data, we established an intelligent rock mass classification model using the random forest algorithm. This model demonstrated good accuracy and generalization performance, with an average accuracy exceeding 0.9. The proposed rock drilling testing system, combined with the intelligent rock mass classification model, forms an integrated system for the intelligent identification of rock mass grades. This system has significant implications for the intelligent and safe construction of drill-and-blast tunnels. [ABSTRACT FROM AUTHOR]

With the surge in Asian-White intermarriages, understanding these couples' experiences is crucial for providing effective clinical guidance in the current racial climate. Interviews with 20 White individuals married to Asian Americans were analyzed. Five major themes emerged from multiple case analyses: cultural differences affecting the couples; cultural differences with extended family and others; racism and the impact of the pandemic; concerns about children; and the benefits and strengths of intermarrying. Clinicians assisting interracial couples may employ these results to tailor their interventions based on the social context. [ABSTRACT FROM AUTHOR]